ObjectiveMagnetoencephalography (MEG), a non-invasive neuroimaging technique, meticulously captures the magnetic fields emanating from brain electrical activity. Compared with MEG based on superconducting quantum interference devices (SQUID), MEG based on optically pump magnetometer (OPM) has the advantages of higher sensitivity, better spatial resolution and lower cost. However, most of the current studies are clinical studies, and there is a lack of animal studies on MEG based on OPM technology. Pain, a multifaceted sensory and emotional phenomenon, induces intricate alterations in brain activity, exhibiting notable sex differences. Despite clinical revelations of pain-related neuronal activity through MEG, specific properties remain elusive, and comprehensive laboratory studies on pain-associated brain activity alterations are lacking. The aim of this study was to investigate the effects of inflammatory pain (induced by Complete Freund’s Adjuvant (CFA)) on brain activity in a rat model using the MEG technique, to analysis changes in brain activity during pain perception, and to explore sex differences in pain-related MEG signaling. MethodsThis study utilized adult male and female Sprague-Dawley rats. Inflammatory pain was induced via intraplantar injection of CFA (100 μl, 50% in saline) in the left hind paw, with control groups receiving saline. Pain behavior was assessed using von Frey filaments at baseline and 1 h post-injection. For MEG recording, anesthetized rats had an OPM positioned on their head within a magnetic shield, undergoing two 15-minute sessions: a 5-minute baseline followed by a 10-minute mechanical stimulation phase. Data analysis included artifact removal and time-frequency analysis of spontaneous brain activity using accumulated spectrograms, generating spectrograms focused on the 4-30 Hz frequency range. ResultsMEG recordings in anesthetized rats during resting states and hind paw mechanical stimulation were compared, before and after saline/CFA injections. Mechanical stimulation elevated alpha activity in both male and female rats pre- and post-saline/CFA injections. Saline/CFA injections augmented average power in both sexes compared to pre-injection states. Remarkably, female rats exhibited higher average spectral power 1 h after CFA injection than after saline injection during resting states. Furthermore, despite comparable pain thresholds measured by classical pain behavioral tests post-CFA treatment, female rats displayed higher average power than males in the resting state after CFA injection. ConclusionThese results imply an enhanced perception of inflammatory pain in female rats compared to their male counterparts. Our study exhibits sex differences in alpha activities following CFA injection, highlighting heightened brain alpha activity in female rats during acute inflammatory pain in the resting state. Our study provides a method for OPM-based MEG recordings to be used to study brain activity in anaesthetized animals. In addition, the findings of this study contribute to a deeper understanding of pain-related neural activity and pain sex differences.

[Objective] To explore the predictive value of HALP score for prognosis in patients with multiple myeloma (MM). [Methods] A retrospective analysis was conducted on laboratory indicators and related clinical data of newly diagnosed multiple myeloma (NDMM) patients, treated at the Affiliated Hospital of North Sichuan Medical College from January 2016 to October 2023, prior to their first treatment. The HALP score was calculated, and the optimal cutoff value for HALP was determined using X-tile software. Survival analysis was performed using Kaplan-Meier curves for high HALP and low HALP groups. Univariate and multivariate analyses were conducted using the Cox regression model, and a forest plot was generated using Graphpad Prism to illustrate factors that may impact patient prognosis. The predictive ability of HALP score combined with β2-microglobulin and ECOG score for prognosis in MM patients was evaluated using receiver operating characteristic curve (ROC) analysis. [Results] A total of 203 MM patients were included, with the optimal cutoff value for HALP score being 29.15 (P<0.05). Among them, 101 patients were in the low HALP score group, and 102 patients were in the high HALP score group. The results of univariate and multivariate analysis using the Cox regression model showed that a HALP score <29.15 was an independent risk factor for progression-free survival (PFS) and overall survival (OS) (P<0.05). ROC curve analysis indicated that the combination of HALP score with β2-microglobulin and ECOG score had a higher predictive value for prognosis in MM patients compared to using HALP score alone. [Conclusion] The HALP score is closely related to the prognosis of patients with NDMM. A low HALP score indicates a poorer prognosis, while the combination of HALP score with β2-microglobulin and ECOG score provides a higher predictive value when assessed together.

Objective: To understand the prevalence and related factors of depressive symptoms among primary and secondary school students in the in depth monitoring counties of China s Rural Compulsory Education Nutrition Improvement Program, so as to provide a basis for prevention and psychological intervention of depressive symptoms among children and adolescents in rural areas. Methods: In November 2022, a stratified random sampling method was adopted to collect height and weight data, basic personal and family information of 7 949 primary and secondary school students from grade three to grade nine through physical measurements and questionnaires in 56 key monitoring schools implementing the Student Nutrition Improvement Program in 7 in depth monitoring counties (Jalaid Banner in Inner Mongolia, Jinzhai County in Anhui, Mao Xian in Sichuan, Tiandeng County in Guangxi, Mian County in Shaanxi, Zhaozhou County in Heilongjiang and Youxi County in Fujian), and to obtain the information related to their depressive symptoms through the self assessment questionnaire on depression. Multivariate Logistic regression analysis was conducted to analyze the prevalence of depressive symptoms among primary and secondary school students, as well as their related factors. Results: The detection rate of depressive symptoms among primary and secondary school students in the in depth monitored counties was 23.5%. Logistic regression analysis showed that the probability of detecting depressive symptoms was higher among female students, middle school students, students whose video screen duration per day was >2 h, and students whose parents marital status was divorced or widowed ( OR =1.40, 1.64, 1.60, 1.24), and students whose sleep duration reached the recommended standard, whose parents usually accompanied them daily for time was 60-<120 min and ≥120 min, and students whose mothers literacy level was middle school graduation had lower probability of detecting depressive symptoms ( OR =0.85, 0.84, 0.71, 0.76) ( P < 0.05 ). Conclusion The detection rate of depressive symptoms among students in the in depth monitoring area is high, and targeted interventions need to be developed for students to reduce the risk of mental health problems.

ObjectiveTo explore the effect of Buzhong Yiqitang on exercise-induced fatigue and its potential mechanism. MethodsSixty male SPF-grade C57BL/6J mice were randomized into blank， model， low-， medium-， high-dose （4.1， 8.2， 16.4 g·kg^-1， respectively） Buzhong Yiqitang， and vitamin C （0.04 g·kg^-1） groups. The blank and model groups were administrated with normal saline. Each group was administrated with corresponding agents by gavage at a dose of 0.2 mL once a day. Except the blank group， other groups underwent a 6-weeks exhaustive swimming test under negative gravity. At the end of the experiment， blood was collected， and the thymus， spleen， liver， and kidney weights were measured. Serum levels of lactic acid （LD）， blood urea nitrogen （BUN）， creatine kinase （CK）， and malondialdehyde （MDA） were assessed by kits to evaluate fatigue. Hematoxylin-eosin staining was performed to observe pathological changes in the skeletal muscle. Electron microscopy was used to examine the skeletal muscle cell ultrastructure， with a focus on mitochondrial morphological changes. The adenosine triphosphate （ATP） content and activities of mitochondrial respiratory chain complexes Ⅰ， Ⅱ， and Ⅴ in skeletal muscle were determined by kits. The expression levels of key genes and proteins in the PTEN-induced putative kinase 1 （PINK1）-mediated mitochondrial homeostasis pathways in the skeletal muscle were evaluated via Real-time PCR and Western blot， respectively. ResultsCompared with the blank group， the model group showed reductions in weight gain rate （P<0.01） and thymus index （P<0.01）， rises in serum levels of LD， BUN， MDA， and CK （P<0.01）， disarrangement of skeletal muscle， broken muscle fibers， inflammatory cell infiltration in muscle fiber gaps， abnormal morphological changes （increased vacuolated mitochondria and disappearance of cristae） of mitochondria in skeletal muscle cells， and decreased mitochondria. In addition， the skeletal muscle in the model group showed reduced content of ATP， weakened activities of mitochondrial respiratory chain complexes Ⅰ， Ⅱ， and Ⅴ （P<0.05）， up-regulated mRNA levels of PINK1， E3 ubiquitin-protein ligase （Parkin）， hairy/enhancer-of-split related with YRPW motif 1 （HEY1）， dynamin-related protein 1 （Drp1）， sequestosome 1 （p62）， and hypoxia-inducible factor 1 alpha （HIF-1α） （P<0.05）， and down-regulated protein level of microtubule-associated protein 1-light chain 3B （LC3B） （P<0.01）. Compared with the model group， Buzhong Yiqitang prolonged the swimming exhaustion time （P<0.01）， increased the weight gain rate （P<0.01） and thymus index （P<0.01）， lowered the serum levels of LD， BUN， MDA， and CK （P<0.05， P<0.01）. The skeletal muscle in the Buzhong Yiqitang groups showed neat arrangement， reduced inflammatory cells， intact mitochondria with dense cristae， and increased mitochondria. In addition， the skeletal muscle in the Buzhong Yiqitang groups showcased increased ATP content， enhanced activities of mitochondrial respiratory chain complexes Ⅰ， Ⅱ， and Ⅴ （P<0.05， P<0.01）， up-regulated protein levels of PINK1， Parkin， HEY1， LC3B， and Drp1 and mRNA level of HIF-1α （P<0.05， P<0.01）， and down-regulated expression level of p62 （P<0.05， P<0.01）. ConclusionBuzhong Yiqitang can prevent and treat exercise-induced fatigue by regulating the mitochondrial homeostasis of skeletal muscle via the HIF-1α/PINK1/Parkin and HIF-1α/HEY1/PINK1 signaling pathways.

ObjectiveTo explore the effect of Buzhong Yiqitang on exercise-induced fatigue and its potential mechanism. MethodsSixty male SPF-grade C57BL/6J mice were randomized into blank， model， low-， medium-， high-dose （4.1， 8.2， 16.4 g·kg^-1， respectively） Buzhong Yiqitang， and vitamin C （0.04 g·kg^-1） groups. The blank and model groups were administrated with normal saline. Each group was administrated with corresponding agents by gavage at a dose of 0.2 mL once a day. Except the blank group， other groups underwent a 6-weeks exhaustive swimming test under negative gravity. At the end of the experiment， blood was collected， and the thymus， spleen， liver， and kidney weights were measured. Serum levels of lactic acid （LD）， blood urea nitrogen （BUN）， creatine kinase （CK）， and malondialdehyde （MDA） were assessed by kits to evaluate fatigue. Hematoxylin-eosin staining was performed to observe pathological changes in the skeletal muscle. Electron microscopy was used to examine the skeletal muscle cell ultrastructure， with a focus on mitochondrial morphological changes. The adenosine triphosphate （ATP） content and activities of mitochondrial respiratory chain complexes Ⅰ， Ⅱ， and Ⅴ in skeletal muscle were determined by kits. The expression levels of key genes and proteins in the PTEN-induced putative kinase 1 （PINK1）-mediated mitochondrial homeostasis pathways in the skeletal muscle were evaluated via Real-time PCR and Western blot， respectively. ResultsCompared with the blank group， the model group showed reductions in weight gain rate （P<0.01） and thymus index （P<0.01）， rises in serum levels of LD， BUN， MDA， and CK （P<0.01）， disarrangement of skeletal muscle， broken muscle fibers， inflammatory cell infiltration in muscle fiber gaps， abnormal morphological changes （increased vacuolated mitochondria and disappearance of cristae） of mitochondria in skeletal muscle cells， and decreased mitochondria. In addition， the skeletal muscle in the model group showed reduced content of ATP， weakened activities of mitochondrial respiratory chain complexes Ⅰ， Ⅱ， and Ⅴ （P<0.05）， up-regulated mRNA levels of PINK1， E3 ubiquitin-protein ligase （Parkin）， hairy/enhancer-of-split related with YRPW motif 1 （HEY1）， dynamin-related protein 1 （Drp1）， sequestosome 1 （p62）， and hypoxia-inducible factor 1 alpha （HIF-1α） （P<0.05）， and down-regulated protein level of microtubule-associated protein 1-light chain 3B （LC3B） （P<0.01）. Compared with the model group， Buzhong Yiqitang prolonged the swimming exhaustion time （P<0.01）， increased the weight gain rate （P<0.01） and thymus index （P<0.01）， lowered the serum levels of LD， BUN， MDA， and CK （P<0.05， P<0.01）. The skeletal muscle in the Buzhong Yiqitang groups showed neat arrangement， reduced inflammatory cells， intact mitochondria with dense cristae， and increased mitochondria. In addition， the skeletal muscle in the Buzhong Yiqitang groups showcased increased ATP content， enhanced activities of mitochondrial respiratory chain complexes Ⅰ， Ⅱ， and Ⅴ （P<0.05， P<0.01）， up-regulated protein levels of PINK1， Parkin， HEY1， LC3B， and Drp1 and mRNA level of HIF-1α （P<0.05， P<0.01）， and down-regulated expression level of p62 （P<0.05， P<0.01）. ConclusionBuzhong Yiqitang can prevent and treat exercise-induced fatigue by regulating the mitochondrial homeostasis of skeletal muscle via the HIF-1α/PINK1/Parkin and HIF-1α/HEY1/PINK1 signaling pathways.

To investigate body composition and associated factors in adolescents undergoing long-term regular sports training.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.